Agrupamientos de los barrios de Medellín
Santiago Arboleda Quiroz, Olga Lucía Montoya Pérez, Alberto Ramirez Velasquez, Juan David Tangarife Patino
Semestre 02-2019
En este trabajo se abordará el problema de agrupar los barrios de Medellín de acuerdo a distintas dimensiones y analizar espacialmente las agrupaciones.
Librerias escenciales para el analisis de informacion
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(Utiltae)
library(lazyeval)
library('sqldf')## Loading required package: gsubfn## Loading required package: proto## Loading required package: RSQLitelibrary(rgdal)## Loading required package: sp## rgdal: version: 1.4-8, (SVN revision 845)
## Geospatial Data Abstraction Library extensions to R successfully loaded
## Loaded GDAL runtime: GDAL 2.2.3, released 2017/11/20
## Path to GDAL shared files: C:/Users/sanar/OneDrive/Documentos/R/win-library/3.6/rgdal/gdal
## GDAL binary built with GEOS: TRUE
## Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
## Path to PROJ.4 shared files: C:/Users/sanar/OneDrive/Documentos/R/win-library/3.6/rgdal/proj
## Linking to sp version: 1.3-2El insumo principal de este trabajo son los datos abiertos del portal Medata y en particular la Encuesta de Calidad de Vida Medellín Cómo vamos.
ECV <- read.csv("./dataSet/encuesta_calidad_vida.csv", header = TRUE, sep=";", encoding = "UTF-8")Se procede a normalizar las cabeceras del dataframe del set de datos
ECV <- setNames(ECV,set_dataSet_names(names(ECV)))- Caracterización de las dimensiones para la dimensión ALIMENTACION
summary(ECV)## encuesta barrio comuna
## Min. :1.201e+04 CABECERA SAN ANT DE PR. : 8987 MANRIQUE : 28951
## 1st Qu.:5.812e+07 LAS GRANJAS : 5192 BELEN : 26330
## Median :4.713e+08 MORAVIA : 4537 ARANJUEZ : 24860
## Mean :1.424e+09 SANTO DOMINGO SABIO Nº 1: 4152 POPULAR : 22587
## 3rd Qu.:1.592e+09 POPULAR : 4027 ROBLEDO : 22051
## Max. :7.489e+09 CASTILLA : 3873 DOCE DE OCTUBRE: 21787
## (Other) :299806 (Other) :184008
## estrato hogar persona factor_vivienda
## Min. :1.000 Min. :1.000 Min. : 1.000 Min. : 1.00
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 1.000 1st Qu.: 55.50
## Median :2.000 Median :1.000 Median : 2.000 Median : 59.34
## Mean :2.701 Mean :1.004 Mean : 2.641 Mean : 66.72
## 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.: 4.000 3rd Qu.: 87.35
## Max. :6.000 Max. :4.000 Max. :20.000 Max. :1676.46
##
## factor_persona año p_146 p_147
## Min. : 1.333 Min. :2011 Min. :1.000 Min. : 1.000
## 1st Qu.: 43.421 1st Qu.:2012 1st Qu.:4.000 1st Qu.: 9.000
## Median : 53.650 Median :2014 Median :4.000 Median :10.000
## Mean : 59.315 Mean :2014 Mean :4.355 Mean : 9.272
## 3rd Qu.: 68.625 3rd Qu.:2016 3rd Qu.:5.000 3rd Qu.:10.000
## Max. :1186.000 Max. :2018 Max. :5.000 Max. :11.000
##
## p_148 p_149 p_150 p_151
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## Median :4.000 Median : 1.000 Median :1.0000 Median :0.0000
## Mean :3.816 Mean : 1.079 Mean :0.5367 Mean :0.3462
## 3rd Qu.:4.000 3rd Qu.: 1.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :7.000 Max. :10.000 Max. :4.0000 Max. :4.0000
##
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##
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## p_297 p_298 p_299 p_300 p_301
## -88 : 41519 -88 : 41519 -88 : 41519 -88 :106583 -88 :106583
## 0 :163654 0 :165238 0 :168549 0 : 92607 0 : 93433
## 1 : 27609 1 : 26025 1 : 22714 1 : 33592 1 : 32766
## NULL: 97792 NULL: 97792 NULL: 97792 NULL: 97792 NULL: 97792
##
##
##
## p_302 p_303 p_304 p_306 p_305
## -88 : 79995 -88 :106583 -88 : 80663 Min. :-99.00 -88 :105904
## 0 :123340 0 :111424 0 :140397 1st Qu.:-88.00 0 :114083
## 1 : 29447 1 : 14775 1 : 11722 Median : 1.00 1 : 12795
## NULL: 97792 NULL: 97792 NULL: 97792 Mean :-32.81 NULL: 97792
## 3rd Qu.: 7.00
## Max. : 10.00
##
## p_307 p_308 p_309 p_310
## Min. :-99.000 Min. :-99.000 Min. :-99.00 Min. :-98.00
## 1st Qu.: 1.000 1st Qu.: 1.000 1st Qu.:-88.00 1st Qu.:-88.00
## Median : 1.000 Median : 2.000 Median :-88.00 Median : 0.00
## Mean : -2.012 Mean : -8.778 Mean :-56.97 Mean :-37.56
## 3rd Qu.: 1.000 3rd Qu.: 2.000 3rd Qu.: 1.00 3rd Qu.: 0.00
## Max. : 2.000 Max. : 2.000 Max. : 2.00 Max. : 3.00
##
## p_311 p_312 p_313 p_314
## Min. :-99.00 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.: 0.00 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:2.00
## Median : 0.00 Median :3.000 Median :3.000 Median :3.00
## Mean :-21.95 Mean :3.016 Mean :2.812 Mean :2.96
## 3rd Qu.: 0.00 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00
## Max. : 3.00 Max. :5.000 Max. :5.000 Max. :5.00
##
## p_315 p_316 p_317 p_318
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000
## 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000 1st Qu.:3.000
## Median :4.000 Median :4.000 Median :4.000 Median :4.000
## Mean :3.272 Mean :3.503 Mean :3.414 Mean :3.402
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
##
## p_319 p_320 p_321 p_322 p_323
## Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.000 Min. :1.00
## 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:4.000 1st Qu.:4.000 1st Qu.:3.00
## Median :4.000 Median :3.000 Median :4.000 Median :4.000 Median :4.00
## Mean :3.318 Mean :3.096 Mean :3.828 Mean :3.808 Mean :3.84
## 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:4.00
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.00
##
## p_324 p_325 p_326 p_327 p_328
## -98 : 180 -88 :157395 -88 :177241 1 : 24362 -88 :160519
## -99 : 50 NULL :145693 NULL :145693 2 :160519 NULL :145693
## 1 : 27486 1 : 20581 1 : 2295 NULL:145693 1 : 11679
## 2 :157165 6 : 2134 -77 : 1865 2 : 4915
## NULL:145693 5 : 1654 5 : 909 3 : 2405
## 3 : 1570 4 : 835 4 : 2062
## (Other): 1547 (Other): 1736 (Other): 3301
## p_329 p_330 p_331 p_332
## -88 :160515 1 : 82707 NULL :145693 NULL :145693
## NULL :145693 2 :102174 -88 :102174 -88 :102157
## 3 : 19145 NULL:145693 1 : 32346 3 : 58537
## 4 : 2516 2 : 18579 2 : 12269
## 2 : 2105 3 : 10594 4 : 7342
## 1 : 591 4 : 6886 1 : 4530
## (Other): 9 (Other): 14302 (Other): 46
## p_334 p_333 p_335 p_336 p_338
## NULL :145693 1 : 43063 NULL :145693 1 : 32228 -88 :152637
## -88 :141818 2 :141818 -88 :141818 2 :152653 1 : 5939
## 1 : 20591 NULL:145693 3 : 30490 NULL:145693 2 : 6577
## 2 : 10053 4 : 7316 3 : 17417
## 3 : 4814 2 : 3737 4 : 2311
## 4 : 2981 1 : 1515 NULL:145693
## (Other): 4624 (Other): 5
## p_337 p_339 p_340 p_341 p_342
## -88 :152653 1 : 12044 -88 :172837 -88 :172837 -98 : 29
## NULL :145693 2 :172837 NULL :145693 1 : 582 -99 : 5
## 1 : 17992 NULL:145693 1 : 8675 2 : 1305 1 : 4077
## 2 : 7346 2 : 2051 3 : 8095 2 : 46419
## 3 : 3063 3 : 629 4 : 2062 3 :114284
## 4 : 1485 4 : 285 NULL:145693 4 : 20067
## (Other): 2342 (Other): 404 NULL:145693
## p_1 p_2 p_3 p_4 p_5
## 1 : 76725 -88 :222899 NULL:330574 Min. :5001 Min. :1.000
## 2 :222915 3 : 36540 1st Qu.:5001 1st Qu.:1.000
## NULL: 30934 NULL : 30934 Median :5001 Median :1.000
## 5 : 27312 Mean :5001 Mean :1.084
## 4 : 3848 3rd Qu.:5001 3rd Qu.:1.000
## 9 : 2961 Max. :5001 Max. :3.000
## (Other): 6080
## p_6 p_7 p_8 p_9_1 p_9_2
## Min. : 1.00 AUC2 : 9935 NULL:330574 NULL:330574 NULL:330574
## 1st Qu.: 5.00 0302 : 4158
## Median : 9.00 0607 : 3941
## Mean :13.68 0913 : 3754
## 3rd Qu.:13.00 0405 : 3684
## Max. :90.00 1610 : 3473
## (Other):301629
## p_10 p_11 p_12 p_13
## Min. :1.000 Min. :1.000 Min. : 1.000 NULL :299640
## 1st Qu.:2.000 1st Qu.:1.000 1st Qu.: 3.000 1 : 9225
## Median :2.000 Median :1.000 Median : 4.000 2 : 8243
## Mean :2.701 Mean :1.008 Mean : 4.281 3 : 6255
## 3rd Qu.:3.000 3rd Qu.:1.000 3rd Qu.: 5.000 4 : 3839
## Max. :6.000 Max. :4.000 Max. :20.000 5 : 1861
## (Other): 1511
## p_14 p_15 p_16 p_17
## NULL:330574 Min. :1.000 NULL :286090 Min. : 1.000
## 1st Qu.:1.000 1 : 30557 1st Qu.: 1.000
## Median :2.000 2 : 9234 Median : 3.000
## Mean :1.545 3 : 2568 Mean : 3.258
## 3rd Qu.:2.000 4 : 1124 3rd Qu.: 3.000
## Max. :2.000 5 : 522 Max. :18.000
## (Other): 479
## p_18 p_19 p_20 p_21
## Min. : 0.00 Min. :1899 Min. :-88.000 Min. :-88.00
## 1st Qu.: 19.00 1st Qu.:1961 1st Qu.: 1.000 1st Qu.:-88.00
## Median : 34.00 Median :1980 Median : 1.000 Median :-88.00
## Mean : 36.54 Mean :1977 Mean : 2.083 Mean :-56.81
## 3rd Qu.: 53.00 3rd Qu.:1995 3rd Qu.: 2.000 3rd Qu.: 1.00
## Max. :108.00 Max. :2018 Max. : 6.000 Max. : 20.00
##
## p_22 p_23 p_24 p_25_1
## Min. :-88.00 Min. :-99.0000 Min. :-99.000 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.: 3.0000 1st Qu.: 1.000 1st Qu.:-88.00
## Median :-88.00 Median : 3.0000 Median : 1.000 Median :-88.00
## Mean :-73.18 Mean : 0.6992 Mean : 1.273 Mean :-56.35
## 3rd Qu.:-88.00 3rd Qu.: 3.0000 3rd Qu.: 2.000 3rd Qu.: 3.00
## Max. : 16.00 Max. : 6.0000 Max. : 2.000 Max. : 98.00
##
## p_25_2 p_26 p_27 p_28
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-60.85 Mean :-60.64 Mean :-83.85 Mean :-58.33
## 3rd Qu.: 0.00 3rd Qu.: 1.00 3rd Qu.:-88.00 3rd Qu.: 5.00
## Max. : 11.00 Max. : 11.00 Max. : 5.00 Max. : 99.00
##
## p_29 p_30 p_31 p_32
## Min. : -99 Min. :-99.00 -88 :303089 Min. :-99.00
## 1st Qu.: -88 1st Qu.: 3.00 -77 : 6362 1st Qu.: 1.00
## Median : -88 Median : 11.00 1603 : 1295 Median : 1.00
## Mean : 4118 Mean : 12.15 717 : 575 Mean :-19.78
## 3rd Qu.: 5088 3rd Qu.: 26.00 0717 : 490 3rd Qu.: 2.00
## Max. :99773 Max. :106.00 1108 : 485 Max. : 2.00
## (Other): 18278
## p_33 p_34 p_35 p_36
## Min. :-99.000 Min. :-99.00 Min. :1.000 Min. :1.000
## 1st Qu.: 12.000 1st Qu.:-88.00 1st Qu.:1.000 1st Qu.:1.000
## Median : 12.000 Median :-88.00 Median :1.000 Median :2.000
## Mean : -6.896 Mean :-81.72 Mean :1.092 Mean :1.733
## 3rd Qu.: 12.000 3rd Qu.:-88.00 3rd Qu.:1.000 3rd Qu.:2.000
## Max. : 18.000 Max. : 18.00 Max. :2.000 Max. :2.000
##
## p_37 p_38 p_39 p_40
## Min. :-88.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median : 2.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-22.04 Mean :-86.55 Mean :-87.51 Mean :-87.51
## 3rd Qu.: 2.00 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.:-88.00
## Max. : 2.00 Max. : 22.00 Max. : 10.00 Max. : 13.00
##
## p_41 p_42 p_43 p_44
## Min. :-88.00 Min. :-99.0 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.0 1st Qu.:-88.00 1st Qu.:-88.00
## Median : 2.00 Median :-88.0 Median :-88.00 Median :-88.00
## Mean :-23.66 Mean :-86.4 Mean :-63.92 Mean :-64.32
## 3rd Qu.: 2.00 3rd Qu.:-88.0 3rd Qu.: 1.00 3rd Qu.: 1.00
## Max. : 2.00 Max. : 22.0 Max. : 9.00 Max. : 5.00
##
## p_45 p_46 p_47 p_48
## Min. :-99.000 Min. :-99.000 0 :122009 Min. :-99.00
## 1st Qu.: 2.000 1st Qu.: 1.000 NULL : 97792 1st Qu.:-88.00
## Median : 3.000 Median : 3.000 2 : 17201 Median :-88.00
## Mean : 2.746 Mean : 2.104 1 : 16024 Mean :-70.92
## 3rd Qu.: 4.000 3rd Qu.: 4.000 3 : 13099 3rd Qu.:-88.00
## Max. : 10.000 Max. : 13.000 7 : 10676 Max. : 16.00
## (Other): 53773
## p_50 p_49 p_51 p_52
## Min. :-99.000 Min. :-99.00 Min. :-99.000 Min. :-99.000
## 1st Qu.: 2.000 1st Qu.: 1.00 1st Qu.: 2.000 1st Qu.: 2.000
## Median : 2.000 Median : 1.00 Median : 2.000 Median : 2.000
## Mean : 1.905 Mean : -5.26 Mean : 1.895 Mean : 1.883
## 3rd Qu.: 2.000 3rd Qu.: 1.00 3rd Qu.: 2.000 3rd Qu.: 2.000
## Max. : 2.000 Max. : 2.00 Max. : 2.000 Max. : 2.000
##
## p_53 p_54 p_55 p_56
## Min. :-99.000 Min. :-99.000 Min. :-99.000 Min. :-99.000
## 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 2.000 1st Qu.: 2.000
## Median : 2.000 Median : 2.000 Median : 2.000 Median : 2.000
## Mean : 1.896 Mean : 1.891 Mean : 1.873 Mean : 1.887
## 3rd Qu.: 2.000 3rd Qu.: 2.000 3rd Qu.: 2.000 3rd Qu.: 2.000
## Max. : 2.000 Max. : 2.000 Max. : 2.000 Max. : 2.000
##
## p_58 p_57 p_59 p_60
## Min. :-88.00 -98 : 68 Min. :-88.00 Min. :-88.00
## 1st Qu.:-88.00 -99 : 23 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 1 : 442 Median :-88.00 Median :-88.00
## Mean :-44.28 2 :232249 Mean :-59.78 Mean :-59.79
## 3rd Qu.: 1.00 NULL: 97792 3rd Qu.: 0.00 3rd Qu.: 0.00
## Max. : 24.00 Max. : 20.00 Max. : 20.00
##
## p_61 p_62 p_63 p_64_1
## Min. :-88.00 Min. :-88.0 Min. :-88.0 Min. : -99.0
## 1st Qu.:-88.00 1st Qu.:-88.0 1st Qu.:-88.0 1st Qu.: -88.0
## Median :-88.00 Median :-88.0 Median :-88.0 Median : -88.0
## Mean :-59.42 Mean :-60.1 Mean :-60.1 Mean : 562.1
## 3rd Qu.: 1.00 3rd Qu.: 0.0 3rd Qu.: 0.0 3rd Qu.:1981.0
## Max. : 18.00 Max. : 15.0 Max. : 13.0 Max. :2018.0
##
## p_64_2 p_65 p_66 p_67
## Min. : -99.00 Min. :-99.00 Min. :-99.000 Min. :-99.00
## 1st Qu.: -88.00 1st Qu.:-88.00 1st Qu.: 1.000 1st Qu.: 1.00
## Median : -88.00 Median :-88.00 Median : 2.000 Median : 2.00
## Mean : -58.57 Mean :-79.32 Mean : 2.248 Mean :-11.39
## 3rd Qu.: 3.00 3rd Qu.:-88.00 3rd Qu.: 3.000 3rd Qu.: 2.00
## Max. :2006.00 Max. : 8.00 Max. : 7.000 Max. : 2.00
##
## p_68 p_69 p_70 p_71
## Min. :-99.00 Min. :-99.000 Min. :-99.00 Min. :-99.00
## 1st Qu.: 1.00 1st Qu.: 1.000 1st Qu.:-88.00 1st Qu.:-88.00
## Median : 2.00 Median : 3.000 Median :-88.00 Median :-88.00
## Mean :-11.39 Mean : -4.422 Mean :-47.45 Mean :-49.31
## 3rd Qu.: 2.00 3rd Qu.: 4.000 3rd Qu.: 2.00 3rd Qu.: 2.00
## Max. : 2.00 Max. : 8.000 Max. : 2.00 Max. : 2.00
##
## p_72 p_73 p_74 p_75
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-49.85 Mean :-50.27 Mean :-53.68 Mean :-84.15
## 3rd Qu.: 2.00 3rd Qu.: 2.00 3rd Qu.: 2.00 3rd Qu.:-88.00
## Max. : 2.00 Max. : 2.00 Max. : 2.00 Max. : 18.00
##
## p_76 p_77 p_78 p_79
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-86.13 Mean :-87.43 Mean :-87.57 Mean :-87.63
## 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.:-88.00
## Max. : 2.00 Max. : 2.00 Max. : 2.00 Max. : 12.00
##
## p_80 p_81 p_82_1 p_82_2
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. : -99
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.: -88
## Median :-88.00 Median :-88.00 Median :-88.00 Median : -88
## Mean :-83.26 Mean :-22.79 Mean :-50.61 Mean : 1998
## 3rd Qu.:-88.00 3rd Qu.: 16.00 3rd Qu.: 5.00 3rd Qu.: 5001
## Max. : 2.00 Max. :632.00 Max. : 99.00 Max. :99624
##
## p_83 p_84 p_85 p_86
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-51.65 Mean :-51.92 Mean :-50.23 Mean :-52.01
## 3rd Qu.: 4.00 3rd Qu.: 1.00 3rd Qu.: 6.00 3rd Qu.: 1.00
## Max. : 12.00 Max. : 5.00 Max. : 9.00 Max. : 11.00
##
## p_86_1 p_87 p_88 p_89
## -88 :330290 Min. : -99 Min. : -99 Min. : -99
## -98 : 89 1st Qu.: -88 1st Qu.: -88 1st Qu.: -88
## -99 : 70 Median : -88 Median : -88 Median : -88
## NULL: 125 Mean : 250212 Mean : 1355 Mean : 535
## 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.: 0
## Max. :99999999 Max. :5000000 Max. :5000000
##
## p_90 p_91 p_92 p_93
## Min. : -99 Min. : -99 Min. : -99 Min. : -99
## 1st Qu.: -88 1st Qu.: -88 1st Qu.: -88 1st Qu.: -88
## Median : -88 Median : -88 Median : -88 Median : -88
## Mean : 99394 Mean : 8533 Mean : 11597 Mean : 14876
## 3rd Qu.: -88 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.: 0
## Max. :99999999 Max. :35000000 Max. :99999999 Max. :99999999
##
## p_94 p_95 p_95_1 p_96
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-34.68 Mean :-52.12 Mean :-87.59 Mean :-52.58
## 3rd Qu.: 48.00 3rd Qu.: 2.00 3rd Qu.:-88.00 3rd Qu.: 2.00
## Max. : 84.00 Max. : 2.00 Max. : 3.00 Max. : 2.00
##
## p_97 p_98 p_99 p_100
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-84.88 Mean :-84.96 Mean :-84.94 Mean :-55.41
## 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.: 2.00
## Max. : 2.00 Max. : 2.00 Max. : 2.00 Max. : 2.00
##
## p_101 p_102 p_103 p_104
## Min. :-99.00 Min. :-99.00 Min. :-99.0 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.0 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.0 Median :-88.00
## Mean :-81.47 Mean :-82.78 Mean :-81.5 Mean :-85.66
## 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.:-88.0 3rd Qu.:-88.00
## Max. : 11.00 Max. : 11.00 Max. : 2.0 Max. : 12.00
##
## p_105 p_106 p_107 p_108
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-83.44 Mean :-82.88 Mean :-81.95 Mean :-83.43
## 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.:-88.00
## Max. : 2.00 Max. : 52.00 Max. : 84.00 Max. : 54.00
##
## p_109 p_110 p_111 p_112
## Min. :-99.0 Min. :-99.00 Min. :-99.00 Min. : -99
## 1st Qu.:-88.0 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.: -88
## Median :-88.0 Median :-88.00 Median :-88.00 Median : -88
## Mean :-83.9 Mean :-83.86 Mean :-84.71 Mean : 2412
## 3rd Qu.:-88.0 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.: -88
## Max. : 4.0 Max. : 2.00 Max. : 6.00 Max. :15000000
##
## p_113 p_114 p_115 p_116
## Min. : -99.0 Min. : -99 Min. : -99 Min. :-99.00
## 1st Qu.: -88.0 1st Qu.: -88 1st Qu.: -88 1st Qu.:-88.00
## Median : -88.0 Median : -88 Median : -88 Median :-88.00
## Mean : 210.1 Mean : 744 Mean : 3114 Mean :-45.91
## 3rd Qu.: -88.0 3rd Qu.: -88 3rd Qu.: -88 3rd Qu.: 2.00
## Max. :3000000.0 Max. :13500000 Max. :72000000 Max. : 2.00
##
## p_117 p_118 p_119 p_120
## Min. :-99.00 Min. :-99.00 Min. :-99.00 Min. :-99.00
## 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:-88.00
## Median :-88.00 Median :-88.00 Median :-88.00 Median :-88.00
## Mean :-70.96 Mean :-71.01 Mean :-62.74 Mean :-84.95
## 3rd Qu.:-88.00 3rd Qu.:-88.00 3rd Qu.: 2.00 3rd Qu.:-88.00
## Max. : 4.00 Max. : 2.00 Max. : 2.00 Max. : 4.00
##
## p_121 p_122 p_124 p_123
## Min. : -99 Min. : -99 Min. : -99 Min. : -99
## 1st Qu.: -88 1st Qu.: -88 1st Qu.: -88 1st Qu.: -88
## Median : -88 Median : -88 Median : -88 Median : -88
## Mean : 8890 Mean : 12869 Mean : 38428 Mean : 79627
## 3rd Qu.: -88 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.: 0
## Max. :99999999 Max. :99999999 Max. :99999999 Max. :99999999
##
## p_125 p_126 p_127 p_128
## Min. : -99 Min. : -99 Min. :1.000 Min. :1.00
## 1st Qu.: -88 1st Qu.: -88 1st Qu.:2.000 1st Qu.:2.00
## Median : -88 Median : -88 Median :2.000 Median :2.00
## Mean : 8599 Mean : 3385 Mean :1.978 Mean :1.97
## 3rd Qu.: 0 3rd Qu.: 0 3rd Qu.:2.000 3rd Qu.:2.00
## Max. :50000000 Max. :50000000 Max. :2.000 Max. :2.00
##
## p_129 p_130 p_131 p_132
## Min. :1.000 Min. :1.000 Min. :-88.00 Min. :1.000
## 1st Qu.:2.000 1st Qu.:2.000 1st Qu.:-88.00 1st Qu.:2.000
## Median :2.000 Median :2.000 Median :-88.00 Median :2.000
## Mean :1.939 Mean :1.955 Mean :-83.92 Mean :1.929
## 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:-88.00 3rd Qu.:2.000
## Max. :2.000 Max. :2.000 Max. : 4.00 Max. :2.000
##
## p_133 p_134 p_135 p_136
## Min. :-88.00 Min. :1.000 Min. :-88.00 Min. :1.000
## 1st Qu.:-88.00 1st Qu.:2.000 1st Qu.:-88.00 1st Qu.:2.000
## Median :-88.00 Median :2.000 Median :-88.00 Median :2.000
## Mean :-81.63 Mean :1.817 Mean :-71.55 Mean :1.962
## 3rd Qu.:-88.00 3rd Qu.:2.000 3rd Qu.:-88.00 3rd Qu.:2.000
## Max. : 4.00 Max. :2.000 Max. : 4.00 Max. :2.000
##
## p_137 p_138 p_139 p_140
## Min. :-88.00 Min. :1.000 Min. :-88.00 Min. :1.000
## 1st Qu.:-88.00 1st Qu.:2.000 1st Qu.:-88.00 1st Qu.:2.000
## Median :-88.00 Median :2.000 Median :-88.00 Median :2.000
## Mean :-84.57 Mean :1.981 Mean :-86.28 Mean :1.972
## 3rd Qu.:-88.00 3rd Qu.:2.000 3rd Qu.:-88.00 3rd Qu.:2.000
## Max. : 4.00 Max. :2.000 Max. : 4.00 Max. :2.000
##
## p_141 p_142 p_143 p_144
## Min. :-88.00 Min. :-88.000 Min. :-88.0 Min. :-88.000
## 1st Qu.:-88.00 1st Qu.: 2.000 1st Qu.:-88.0 1st Qu.: 2.000
## Median :-88.00 Median : 2.000 Median :-88.0 Median : 2.000
## Mean :-85.47 Mean : 1.984 Mean :-86.7 Mean : 1.975
## 3rd Qu.:-88.00 3rd Qu.: 2.000 3rd Qu.:-88.0 3rd Qu.: 2.000
## Max. : 4.00 Max. : 2.000 Max. : 4.0 Max. : 2.000
##
## p_145
## Min. :-88.00
## 1st Qu.:-88.00
## Median :-88.00
## Mean :-85.71
## 3rd Qu.:-88.00
## Max. : 4.00
## Para la dimensión de MOVILIDAD se toman las preguntas: P_83 ¿Cuál es el medio de transporte utilizado predominante para dirigirse al sitio de su empleo principal? P_84 ¿Cuánto tiempo tardan en llegar al sitio de trabajo? P_212 ¿Cuántos vehículos particulares en funcionamiento tiene este hogar? (no incluye vehículo de servicio público o utilizado para generar ingresos) Vehiculo o auto P_213 ¿Cuántos vehículos particulares en funcionamiento tiene este hogar? (no incluye vehículo de servicio público o utilizado para generar ingresos) Moto o motoneta P_214 ¿Cuántos vehículos particulares en funcionamiento tiene este hogar? (no incluye vehículo de servicio público o utilizado para generar ingresos) Bicicleta P_318 Califique Usted en una escala desde 1 hasta 5, la situación en su barrio o vereda, sobre: La pavimentación y señalización de las vias P_321 Califique Usted, en una escala desde 1 hasta 5, la cobertura en el transporte público en su sector, barrio o vereda en los últimos 12 meses P_322 Y califique en una escala desde 1 hasta 5, la calidad de ese transporte público en su sector, barrio o vereda
ECV_MOVILIDAD<- ECV[,c("encuesta","comuna","barrio","estrato","p_83","p_84","p_212","p_213","p_214","p_318","p_321","p_322")]Revisemos como es el comportamiento de los datos seleccionados
summary(ECV_MOVILIDAD)## encuesta comuna barrio
## Min. :1.201e+04 MANRIQUE : 28951 CABECERA SAN ANT DE PR. : 8987
## 1st Qu.:5.812e+07 BELEN : 26330 LAS GRANJAS : 5192
## Median :4.713e+08 ARANJUEZ : 24860 MORAVIA : 4537
## Mean :1.424e+09 POPULAR : 22587 SANTO DOMINGO SABIO Nº 1: 4152
## 3rd Qu.:1.592e+09 ROBLEDO : 22051 POPULAR : 4027
## Max. :7.489e+09 DOCE DE OCTUBRE: 21787 CASTILLA : 3873
## (Other) :184008 (Other) :299806
## estrato p_83 p_84 p_212
## Min. :1.000 Min. :-99.00 Min. :-99.00 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:0.0000
## Median :2.000 Median :-88.00 Median :-88.00 Median :0.0000
## Mean :2.701 Mean :-51.65 Mean :-51.92 Mean :0.1965
## 3rd Qu.:3.000 3rd Qu.: 4.00 3rd Qu.: 1.00 3rd Qu.:0.0000
## Max. :6.000 Max. : 12.00 Max. : 5.00 Max. :6.0000
##
## p_213 p_214 p_318 p_321
## Min. :0.0000 Min. :0.0000 Min. :1.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:4.000
## Median :0.0000 Median :0.0000 Median :4.000 Median :4.000
## Mean :0.2356 Mean :0.2596 Mean :3.402 Mean :3.828
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.0000 Max. :9.0000 Max. :5.000 Max. :5.000
##
## p_322
## Min. :1.000
## 1st Qu.:4.000
## Median :4.000
## Mean :3.808
## 3rd Qu.:4.000
## Max. :5.000
## Eliminamos las filas dónde todas las columnas sean NULL
ECV_MOVILIDAD_DEP <- subset(ECV_MOVILIDAD,p_83 != 'NULL' & p_84 != 'NULL' & p_212 != 'NULL' & p_213 != 'NULL' & p_214 != 'NULL' & p_318 != 'NULL' & p_321 != 'NULL' & p_322 != 'NULL')
nrow(ECV_MOVILIDAD_DEP)## [1] 330574summary(ECV_MOVILIDAD_DEP)## encuesta comuna barrio
## Min. :1.201e+04 MANRIQUE : 28951 CABECERA SAN ANT DE PR. : 8987
## 1st Qu.:5.812e+07 BELEN : 26330 LAS GRANJAS : 5192
## Median :4.713e+08 ARANJUEZ : 24860 MORAVIA : 4537
## Mean :1.424e+09 POPULAR : 22587 SANTO DOMINGO SABIO Nº 1: 4152
## 3rd Qu.:1.592e+09 ROBLEDO : 22051 POPULAR : 4027
## Max. :7.489e+09 DOCE DE OCTUBRE: 21787 CASTILLA : 3873
## (Other) :184008 (Other) :299806
## estrato p_83 p_84 p_212
## Min. :1.000 Min. :-99.00 Min. :-99.00 Min. :0.0000
## 1st Qu.:2.000 1st Qu.:-88.00 1st Qu.:-88.00 1st Qu.:0.0000
## Median :2.000 Median :-88.00 Median :-88.00 Median :0.0000
## Mean :2.701 Mean :-51.65 Mean :-51.92 Mean :0.1965
## 3rd Qu.:3.000 3rd Qu.: 4.00 3rd Qu.: 1.00 3rd Qu.:0.0000
## Max. :6.000 Max. : 12.00 Max. : 5.00 Max. :6.0000
##
## p_213 p_214 p_318 p_321
## Min. :0.0000 Min. :0.0000 Min. :1.000 Min. :1.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:4.000
## Median :0.0000 Median :0.0000 Median :4.000 Median :4.000
## Mean :0.2356 Mean :0.2596 Mean :3.402 Mean :3.828
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:4.000 3rd Qu.:4.000
## Max. :5.0000 Max. :9.0000 Max. :5.000 Max. :5.000
##
## p_322
## Min. :1.000
## 1st Qu.:4.000
## Median :4.000
## Mean :3.808
## 3rd Qu.:4.000
## Max. :5.000
## Definiciones:
Solucion de Preguntas - Consultas
Dimensión Movilidad.
P_83: ¿Cuál es el medio de transporte utilizado predominante para dirigirse al sitio de su empleo principal? Indicador: Porcentaje de personas que usan bicicleta para ir a su empleo principal Formula: Total de personas BICICLETA / Total Encuestados P_83 *100
# CAST((SUM(p_83) / totalEncuestadosE * 100) AS DOUBLE)
ECV_MOVILIDAD_P83 <- get_indicadores_ei(ECV_MOVILIDAD_DEP,'p_83')
CONS_P83 <- sqldf("SELECT comuna, barrio, CAST (SUM(totalRespuestaE) AS REAL) / TotalB *100 AS P83_IND
FROM ECV_MOVILIDAD_P83
WHERE p_83 = '2'
GROUP BY comuna, barrio")
#CONS_P83["ind"] <- CONS_P83$`SUM(totalRespuestaE)` / CONS_P83$TotalB * 100P_84: ¿Cuánto tiempo tardan en llegar al sitio de trabajo? Indicador: Porcentaje de personas que tardan en llegar al trabajo mas de una hora Formula: Sumatoria de los tiempos / Total Encuestados P_84
ECV_MOVILIDAD_P84 <- get_indicadores_ei(ECV_MOVILIDAD_DEP,'p_84')
CONS_P84 <- sqldf("SELECT comuna, barrio, CAST (SUM(totalRespuestaE) AS REAL) / TotalB *100 AS P84_IND
FROM ECV_MOVILIDAD_P84
WHERE p_84 = '4' OR p_84 = '5'
GROUP BY comuna, barrio")
#CONS_P84["P84_IND"] <- CONS_P84$`SUM(totalRespuestaE)` / CONS_P84$TotalB * 100P_212: ¿Cuántos vehículos particulares en funcionamiento tiene este hogar? (no incluye vehículo de servicio público o utilizado para generar ingresos) Vehiculo o auto Indicador: Promedio de vehiculos en el hogar (Vehiculo o auto) Formula: Sumatoria de las respuestas Vehiculo o auto / Total Encuestados P_212
ECV_MOVILIDAD_P212 <- get_indicadores_ec(ECV_MOVILIDAD_DEP,'p_212')
CONS_P212 <- sqldf("SELECT comuna, barrio, SUM(cast ((p_212) as real)*(totalRespuestaE))/TotalB AS P212_IND
FROM ECV_MOVILIDAD_P212
WHERE p_212 > 0
GROUP BY comuna, barrio")
#CONS_P212["ind"] <- CONS_P212$nuevoP_213: ¿Cuántos vehículos particulares en funcionamiento tiene este hogar? (no incluye vehículo de servicio público o utilizado para generar ingresos) Moto o motoneta Indicador: Promedio de vehiculos en el hogar (Moto) Formula: Sumatoria de las respuestas Moto / Total Encuestados P_213
ECV_MOVILIDAD_P213 <- get_indicadores_ec(ECV_MOVILIDAD_DEP,'p_213')
CONS_P213 <- sqldf("SELECT comuna, barrio, SUM(cast ((p_213) as real)*(totalRespuestaE))/TotalB AS P213_IND
FROM ECV_MOVILIDAD_P213
WHERE p_213 > 0
GROUP BY comuna, barrio")
#CONS_P212["ind"] <- CONS_P213$`SUM(p_213)` / CONS_P213$`SUM(totalEncuestadosE)` #ECV_MOVILIDAD_P213 %>% group_by(barrio) %>% summarise(sum(p_213 * totalRespuestaE)/sum(totalRespuestaE) ) -> data_hogarP_214: ¿Cuántos vehículos particulares en funcionamiento tiene este hogar? (no incluye vehículo de servicio público o utilizado para generar ingresos) Bicicleta Indicador: Promedio de vehiculos en el hogar (Bicicleta) Formula: Sumatoria de las respuestas Bicicleta / Total Encuestados P_214
ECV_MOVILIDAD_P214 <- get_indicadores_ec(ECV_MOVILIDAD_DEP,'p_214')
CONS_P214 <- sqldf("SELECT comuna, barrio, SUM(cast ((p_214) as real)*(totalRespuestaE))/TotalB AS P214_IND
FROM ECV_MOVILIDAD_P214
WHERE p_214 > 0
GROUP BY comuna, barrio")P_318: Califique Usted en una escala desde 1 hasta 5, la situación en su barrio o vereda, sobre: La pavimentación y señalización de las vias Indicador: Calificación promedio del estado de la infraestrucutra vial Formula: Sumatoria de las Calificaciones / Total Encuestados P_318
ECV_MOVILIDAD_P318 <- get_indicadores_ei(ECV_MOVILIDAD_DEP,'p_318')
CONS_P318 <- sqldf("SELECT comuna, barrio, SUM(cast ((p_318) as real)*(totalRespuestaE)) / sum(totalRespuestaE) AS P318_IND
FROM ECV_MOVILIDAD_P318
WHERE p_318 > 0
GROUP BY comuna, barrio")P_321: Califique Usted, en una escala desde 1 hasta 5, la cobertura en el transporte público en su sector, barrio o vereda en los últimos 12 meses Indicador: Calificación promedio de la cobertura del transporte público Formula: Sumatoria de las respuestas “Muy buena” / Total Encuestados P_321 *100
ECV_MOVILIDAD_P321 <- get_indicadores_ei(ECV_MOVILIDAD_DEP,'p_321')
CONS_P321 <- sqldf("SELECT comuna, barrio, SUM(cast ((p_321) as real)*(totalRespuestaE)) / sum(totalRespuestaE) AS P321_IND
FROM ECV_MOVILIDAD_P321
WHERE p_321 > 0
GROUP BY comuna, barrio")P_322: Y califique en una escala desde 1 hasta 5, la calidad de ese transporte público en su sector, barrio o vereda Indicador: Porcentaje de personas que piensan que la calidad del transporte publico en el sector es “Aceptable” Formula: Sumatoria de las respuestas Aceptable" / Total Encuestados P_322 *100
ECV_MOVILIDAD_P322 <- get_indicadores_ei(ECV_MOVILIDAD_DEP,'p_322')
CONS_P322 <- sqldf("SELECT comuna, barrio, SUM(cast ((p_322) as real)*(totalRespuestaE)) / sum(totalRespuestaE) AS P322_IND
FROM ECV_MOVILIDAD_P322
WHERE p_322 > 0
GROUP BY comuna, barrio")ECV_MOVILIDAD_FINAL <- data.frame(unique(ECV_MOVILIDAD[,c("comuna", "barrio")]))
ECV_MOVILIDAD_FINAL <- merge(ECV_MOVILIDAD_FINAL, CONS_P83, by = c("comuna", "barrio"), all.x = TRUE)
ECV_MOVILIDAD_FINAL <- merge(ECV_MOVILIDAD_FINAL, CONS_P84, by = c("comuna", "barrio"), all.x = TRUE)
ECV_MOVILIDAD_FINAL <- merge(ECV_MOVILIDAD_FINAL, CONS_P212, by = c("comuna", "barrio"), all.x = TRUE)
ECV_MOVILIDAD_FINAL <- merge(ECV_MOVILIDAD_FINAL, CONS_P213, by = c("comuna", "barrio"), all.x = TRUE)
ECV_MOVILIDAD_FINAL <- merge(ECV_MOVILIDAD_FINAL, CONS_P214, by = c("comuna", "barrio"), all.x = TRUE)
ECV_MOVILIDAD_FINAL <- merge(ECV_MOVILIDAD_FINAL, CONS_P318, by = c("comuna", "barrio"), all.x = TRUE)
ECV_MOVILIDAD_FINAL <- merge(ECV_MOVILIDAD_FINAL, CONS_P321, by = c("comuna", "barrio"), all.x = TRUE)
ECV_MOVILIDAD_FINAL <- merge(ECV_MOVILIDAD_FINAL, CONS_P322, by = c("comuna", "barrio"), all.x = TRUE)ECV_MOVILIDAD_FINAL[is.na(ECV_MOVILIDAD_FINAL)] <- 0
ECV_MOVILIDAD_FINAL$barrio <- paste(ECV_MOVILIDAD_FINAL$comuna, ECV_MOVILIDAD_FINAL$barrio, sep = "/")
ECV_MOVILIDAD_FINAL$comuna <- NULLFin Preguntas
Agrupamiento
K-MEANS
Normalizamos los datos
ECV_MOVILIDAD_SCALE <- tibble::column_to_rownames(ECV_MOVILIDAD_FINAL, var = ("barrio"))
ECV_MOVILIDAD_SCALE <- scale(ECV_MOVILIDAD_SCALE)#any(is.na(ECV_MOVILIDAD_FINAL))
#any(is.finite(ECV_VIVIENDA_BARRIO_SCALE))Método del codo para determinar el k optimo
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa# Elbow method
fviz_nbclust(ECV_MOVILIDAD_SCALE, kmeans, method = "wss") +
geom_vline(xintercept = 4, linetype = 2)+
labs(subtitle = "Elbow method")
De acuerdo a la gráfica, donde se da el k con el mínimo error es cuando toma el valor de 4
Método de la siluetta para determinar el k óptimo
fviz_nbclust(ECV_MOVILIDAD_SCALE, kmeans, method = "silhouette")
De acuerdo a la gráfica, donde se da el k con el mínimo error es cuando toma el valor de 2
# Gap statistic
# nboot = 50 to keep the function speedy.
# recommended value: nboot= 500 for your analysis.
# Use verbose = FALSE to hide computing progression.
# The gap statistic compares the total intracluster variation for different values of k with their expected values under null reference distribution of the data (i.e. a distribution with no obvious clustering). The reference dataset is generated using Monte Carlo simulations of the sampling process.
set.seed(123)
fviz_nbclust(ECV_MOVILIDAD_SCALE, kmeans, nstart = 50, method = "gap_stat", nboot = 50)+
labs(subtitle = "Gap statistic method")
SS <- fviz_nbclust(ECV_MOVILIDAD_SCALE, kmeans, nstart = 50, method = "wss", nboot = 50)
plot(2:10, diff(SS$data$y), type="h", main="Diferencia de Errores", xlab="k", ylab="diff")
Aplicamos K-MEANS para el k recomendado por el método del codo
set.seed(42)
kmeans_model <- kmeans(ECV_MOVILIDAD_SCALE, 5, nstart = 50)
#kmeans_modelAgregar el cluster a la data original
df_member <- cbind(ECV_MOVILIDAD_FINAL, cluster = kmeans_model$cluster)
head(df_member)Visualización de los grupos
fviz_cluster(kmeans_model, data = ECV_MOVILIDAD_SCALE)
fviz_cluster(kmeans_model, ECV_MOVILIDAD_SCALE, ellipse.type = "norm")## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
# Cluster size
kmeans_model$size## [1] 173 2 101 33 1aggregate(ECV_MOVILIDAD_FINAL, by=list(cluster=kmeans_model$cluster), mean)## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NA
## Warning in mean.default(X[[i]], ...): argument is not numeric or logical:
## returning NAMapas
library(stringr)
library(tidyr)
#df_ <- str_split_fixed(df_member$barrio, "-", 2)
#head(df_)
#within(df_member, FOO<-data.frame(do.call('rbind', strsplit(as.character(df_member$barrio), '-', fixed=TRUE))))
df_member2 <- separate(df_member, 'barrio', paste("barrio", 1:2, sep="_"), sep="/", extra="drop")barrios_med <- readOGR("Barrio_Vereda/Barrio_Vereda.shp",layer="Barrio_Vereda")## OGR data source with driver: ESRI Shapefile
## Source: "D:\UNAL\Tecnicas de aprendizaje estadistico\Trabajo 1\TAE_T1\Barrio_Vereda\Barrio_Vereda.shp", layer: "Barrio_Vereda"
## with 332 features
## It has 6 fields
## Integer64 fields read as strings: OBJECTID SUBTIPO_BAnombres_barrios <- iconv(barrios_med@data$NOMBRE,"UTF-8","ISO_8859-1")
#head(nombres_barrios)sub_ <- df_member2[,c("barrio_2","cluster")]rownames(sub_) <- NULL
#head(sub_)sub_$barrio_2 <- tolower(sub_$barrio_2)
#head(sub_)Caps <- function(x) {
s <- strsplit(x, " ")[[1]]
paste(toupper(substring(s, 1,1)), substring(s, 2),
sep="", collapse=" ")
}
sub_$barrio_2 <- sapply(sub_$barrio_2, Caps)
#head(sub_)#nom_bar <- merge(barrios_med, sub_, by.x="NOMBRE", by.y="barrio_2", duplicateGeoms = T, all.x = TRUE)
#names(nom_bar)library(dplyr)
# union two dataframes without duplicates
nom_bar <- merge(barrios_med, sub_[!duplicated(sub_$barrio_2), ], by.x="NOMBRE", by.y="barrio_2", all.x = TRUE)
names(nom_bar)## [1] "NOMBRE" "OBJECTID" "CODIGO" "SUBTIPO_BA" "SHAPEAREA"
## [6] "SHAPELEN" "cluster"head(nom_bar)## An object of class "SpatialPolygonsDataFrame"
## Slot "data":
## NOMBRE OBJECTID CODIGO SUBTIPO_BA SHAPEAREA SHAPELEN
## 141 La Aguacatala 661 1422 1 622090.2 3302.658
## 99 El Pinal 662 0810 1 413416.8 3271.575
## 127 Fuente Clara 663 0719 1 236441.2 3022.338
## 297 Santo Domingo Savio No.2 664 0102 1 264750.5 2943.708
## 188 Las Granjas 665 0302 1 641349.3 3964.702
## 30 Barrios de Jesús 666 0902 1 442235.7 3754.624
## cluster
## 141 3
## 99 1
## 127 4
## 297 NA
## 188 1
## 30 NA
##
## Slot "polygons":
## [[1]]
## An object of class "Polygons"
## Slot "Polygons":
## [[1]]
## An object of class "Polygon"
## Slot "labpt":
## [1] -75.577522 6.198658
##
## Slot "area":
## [1] 5.080424e-05
##
## Slot "hole":
## [1] FALSE
##
## Slot "ringDir":
## [1] 1
##
## Slot "coords":
## [,1] [,2]
## [1,] -75.57623 6.194622
## [2,] -75.57630 6.194468
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## [1635,] -75.53732 6.227554
## [1636,] -75.53733 6.227548
## [1637,] -75.53733 6.227541
## [1638,] -75.53734 6.227527
## [1639,] -75.53733 6.227493
## [1640,] -75.53733 6.227487
## [1641,] -75.53733 6.227484
## [1642,] -75.53734 6.227480
## [1643,] -75.53739 6.227490
## [1644,] -75.53740 6.227490
## [1645,] -75.53740 6.227491
## [1646,] -75.53741 6.227490
## [1647,] -75.53742 6.227490
## [1648,] -75.53743 6.227489
## [1649,] -75.53743 6.227488
## [1650,] -75.53744 6.227487
## [1651,] -75.53745 6.227486
## [1652,] -75.53746 6.227484
## [1653,] -75.53747 6.227483
## [1654,] -75.53748 6.227484
## [1655,] -75.53748 6.227484
## [1656,] -75.53748 6.227484
## [1657,] -75.53749 6.227484
## [1658,] -75.53750 6.227486
## [1659,] -75.53750 6.227486
## [1660,] -75.53752 6.227489
## [1661,] -75.53753 6.227493
## [1662,] -75.53754 6.227498
## [1663,] -75.53754 6.227498
## [1664,] -75.53755 6.227501
## [1665,] -75.53756 6.227503
## [1666,] -75.53756 6.227504
## [1667,] -75.53757 6.227508
## [1668,] -75.53759 6.227510
## [1669,] -75.53759 6.227511
## [1670,] -75.53760 6.227511
## [1671,] -75.53760 6.227511
## [1672,] -75.53761 6.227511
## [1673,] -75.53762 6.227508
## [1674,] -75.53763 6.227504
## [1675,] -75.53763 6.227502
## [1676,] -75.53764 6.227500
## [1677,] -75.53764 6.227498
## [1678,] -75.53765 6.227498
## [1679,] -75.53766 6.227499
## [1680,] -75.53768 6.227494
## [1681,] -75.53769 6.227487
## [1682,] -75.53771 6.227481
## [1683,] -75.53772 6.227480
## [1684,] -75.53772 6.227479
## [1685,] -75.53773 6.227477
## [1686,] -75.53773 6.227477
## [1687,] -75.53774 6.227476
## [1688,] -75.53774 6.227475
## [1689,] -75.53774 6.227470
## [1690,] -75.53775 6.227460
## [1691,] -75.53776 6.227447
## [1692,] -75.53777 6.227439
## [1693,] -75.53777 6.227437
## [1694,] -75.53778 6.227435
## [1695,] -75.53779 6.227433
## [1696,] -75.53780 6.227433
## [1697,] -75.53781 6.227433
## [1698,] -75.53782 6.227432
## [1699,] -75.53783 6.227432
## [1700,] -75.53785 6.227432
## [1701,] -75.53786 6.227432
## [1702,] -75.53786 6.227432
## [1703,] -75.53787 6.227432
## [1704,] -75.53788 6.227430
## [1705,] -75.53789 6.227429
## [1706,] -75.53790 6.227427
## [1707,] -75.53791 6.227424
## [1708,] -75.53792 6.227424
## [1709,] -75.53793 6.227422
## [1710,] -75.53793 6.227422
## [1711,] -75.53793 6.227422
## [1712,] -75.53794 6.227421
## [1713,] -75.53795 6.227420
## [1714,] -75.53796 6.227419
## [1715,] -75.53797 6.227417
## [1716,] -75.53797 6.227416
## [1717,] -75.53797 6.227415
## [1718,] -75.53798 6.227415
## [1719,] -75.53798 6.227414
## [1720,] -75.53798 6.227412
## [1721,] -75.53799 6.227411
## [1722,] -75.53799 6.227409
## [1723,] -75.53799 6.227409
## [1724,] -75.53800 6.227407
## [1725,] -75.53800 6.227407
## [1726,] -75.53801 6.227407
## [1727,] -75.53801 6.227405
## [1728,] -75.53802 6.227401
## [1729,] -75.53803 6.227397
## [1730,] -75.53804 6.227397
## [1731,] -75.53804 6.227397
## [1732,] -75.53805 6.227392
## [1733,] -75.53805 6.227392
## [1734,] -75.53806 6.227392
## [1735,] -75.53806 6.227392
## [1736,] -75.53806 6.227392
## [1737,] -75.53806 6.227392
## [1738,] -75.53807 6.227390
## [1739,] -75.53809 6.227384
## [1740,] -75.53810 6.227375
## [1741,] -75.53811 6.227373
## [1742,] -75.53812 6.227369
## [1743,] -75.53813 6.227365
## [1744,] -75.53813 6.227365
## [1745,] -75.53814 6.227363
## [1746,] -75.53815 6.227362
## [1747,] -75.53817 6.227361
## [1748,] -75.53817 6.227361
## [1749,] -75.53818 6.227362
## [1750,] -75.53819 6.227363
## [1751,] -75.53820 6.227367
## [1752,] -75.53820 6.227370
## [1753,] -75.53821 6.227376
## [1754,] -75.53821 6.227382
## [1755,] -75.53822 6.227387
## [1756,] -75.53823 6.227398
## [1757,] -75.53823 6.227401
## [1758,] -75.53823 6.227408
## [1759,] -75.53823 6.227416
## [1760,] -75.53824 6.227425
## [1761,] -75.53824 6.227428
## [1762,] -75.53825 6.227431
## [1763,] -75.53825 6.227434
## [1764,] -75.53826 6.227436
## [1765,] -75.53826 6.227440
## [1766,] -75.53827 6.227441
## [1767,] -75.53827 6.227444
## [1768,] -75.53829 6.227447
## [1769,] -75.53830 6.227448
## [1770,] -75.53831 6.227448
## [1771,] -75.53831 6.227448
## [1772,] -75.53833 6.227446
## [1773,] -75.53833 6.227445
## [1774,] -75.53834 6.227444
## [1775,] -75.53834 6.227444
## [1776,] -75.53835 6.227444
## [1777,] -75.53835 6.227444
## [1778,] -75.53836 6.227445
## [1779,] -75.53837 6.227451
## [1780,] -75.53837 6.227451
## [1781,] -75.53837 6.227452
## [1782,] -75.53838 6.227460
## [1783,] -75.53839 6.227467
## [1784,] -75.53839 6.227467
## [1785,] -75.53839 6.227473
## [1786,] -75.53840 6.227475
## [1787,] -75.53841 6.227477
## [1788,] -75.53842 6.227476
## [1789,] -75.53844 6.227475
## [1790,] -75.53845 6.227475
## [1791,] -75.53846 6.227475
## [1792,] -75.53847 6.227475
## [1793,] -75.53848 6.227478
## [1794,] -75.53848 6.227479
## [1795,] -75.53849 6.227480
## [1796,] -75.53851 6.227472
## [1797,] -75.53851 6.227469
## [1798,] -75.53852 6.227462
## [1799,] -75.53853 6.227453
## [1800,] -75.53854 6.227449
## [1801,] -75.53854 6.227446
## [1802,] -75.53855 6.227439
## [1803,] -75.53856 6.227435
## [1804,] -75.53856 6.227433
## [1805,] -75.53857 6.227431
## [1806,] -75.53858 6.227429
## [1807,] -75.53859 6.227431
## [1808,] -75.53859 6.227433
## [1809,] -75.53860 6.227434
## [1810,] -75.53861 6.227438
## [1811,] -75.53862 6.227440
## [1812,] -75.53862 6.227441
## [1813,] -75.53863 6.227441
## [1814,] -75.53864 6.227441
## [1815,] -75.53865 6.227441
## [1816,] -75.53866 6.227441
## [1817,] -75.53867 6.227444
## [1818,] -75.53867 6.227445
## [1819,] -75.53868 6.227451
## [1820,] -75.53868 6.227453
## [1821,] -75.53868 6.227453
## [1822,] -75.53868 6.227454
## [1823,] -75.53869 6.227456
## [1824,] -75.53869 6.227456
## [1825,] -75.53869 6.227459
## [1826,] -75.53870 6.227467
## [1827,] -75.53871 6.227479
## [1828,] -75.53871 6.227480
## [1829,] -75.53871 6.227480
## [1830,] -75.53871 6.227489
## [1831,] -75.53871 6.227489
## [1832,] -75.53872 6.227496
## [1833,] -75.53872 6.227499
## [1834,] -75.53873 6.227510
## [1835,] -75.53874 6.227523
## [1836,] -75.53874 6.227528
## [1837,] -75.53875 6.227534
## [1838,] -75.53875 6.227540
## [1839,] -75.53876 6.227544
## [1840,] -75.53876 6.227553
## [1841,] -75.53876 6.227555
## [1842,] -75.53877 6.227563
## [1843,] -75.53877 6.227568
## [1844,] -75.53878 6.227577
## [1845,] -75.53878 6.227580
## [1846,] -75.53879 6.227586
## [1847,] -75.53879 6.227591
## [1848,] -75.53879 6.227597
## [1849,] -75.53880 6.227604
## [1850,] -75.53880 6.227609
## [1851,] -75.53881 6.227618
## [1852,] -75.53881 6.227620
## [1853,] -75.53881 6.227621
## [1854,] -75.53882 6.227628
## [1855,] -75.53883 6.227637
## [1856,] -75.53883 6.227643
## [1857,] -75.53884 6.227647
## [1858,] -75.53884 6.227649
## [1859,] -75.53885 6.227655
## [1860,] -75.53885 6.227661
## [1861,] -75.53885 6.227662
## [1862,] -75.53886 6.227669
## [1863,] -75.53887 6.227676
## [1864,] -75.53888 6.227683
## [1865,] -75.53889 6.227689
## [1866,] -75.53890 6.227694
## [1867,] -75.53891 6.227700
## [1868,] -75.53891 6.227706
## [1869,] -75.53892 6.227712
## [1870,] -75.53893 6.227720
## [1871,] -75.53894 6.227727
## [1872,] -75.53894 6.227728
## [1873,] -75.53894 6.227728
## [1874,] -75.53895 6.227734
## [1875,] -75.53896 6.227740
## [1876,] -75.53896 6.227744
## [1877,] -75.53897 6.227746
## [1878,] -75.53898 6.227751
## [1879,] -75.53898 6.227752
## [1880,] -75.53898 6.227752
## [1881,] -75.53898 6.227755
## [1882,] -75.53899 6.227760
## [1883,] -75.53900 6.227764
## [1884,] -75.53900 6.227767
## [1885,] -75.53901 6.227774
## [1886,] -75.53903 6.227779
## [1887,] -75.53904 6.227784
## [1888,] -75.53904 6.227786
## [1889,] -75.53905 6.227789
## [1890,] -75.53906 6.227794
## [1891,] -75.53906 6.227795
## [1892,] -75.53906 6.227795
## [1893,] -75.53907 6.227800
## [1894,] -75.53907 6.227802
## [1895,] -75.53908 6.227805
## [1896,] -75.53908 6.227809
## [1897,] -75.53908 6.227810
## [1898,] -75.53909 6.227813
## [1899,] -75.53910 6.227815
## [1900,] -75.53910 6.227816
## [1901,] -75.53911 6.227820
## [1902,] -75.53912 6.227826
## [1903,] -75.53913 6.227832
## [1904,] -75.53914 6.227845
## [1905,] -75.53914 6.227853
## [1906,] -75.53914 6.227853
## [1907,] -75.53914 6.227854
## [1908,] -75.53915 6.227859
## [1909,] -75.53915 6.227866
## [1910,] -75.53916 6.227871
## [1911,] -75.53916 6.227876
## [1912,] -75.53918 6.227887
## [1913,] -75.53918 6.227888
## [1914,] -75.53919 6.227900
## [1915,] -75.53920 6.227902
## [1916,] -75.53921 6.227912
## [1917,] -75.53921 6.227922
## [1918,] -75.53921 6.227923
## [1919,] -75.53922 6.227931
## [1920,] -75.53923 6.227941
## [1921,] -75.53923 6.227945
## [1922,] -75.53924 6.227953
## [1923,] -75.53924 6.227958
## [1924,] -75.53924 6.227958
## [1925,] -75.53925 6.227964
## [1926,] -75.53925 6.227967
## [1927,] -75.53925 6.227973
## [1928,] -75.53926 6.227982
## [1929,] -75.53927 6.227987
## [1930,] -75.53928 6.227993
## [1931,] -75.53929 6.228003
## [1932,] -75.53930 6.228015
## [1933,] -75.53931 6.228020
## [1934,] -75.53932 6.228025
## [1935,] -75.53933 6.228032
## [1936,] -75.53934 6.228040
## [1937,] -75.53936 6.228047
## [1938,] -75.53936 6.228049
## [1939,] -75.53937 6.228051
## [1940,] -75.53937 6.228055
## [1941,] -75.53938 6.228062
## [1942,] -75.53938 6.228068
## [1943,] -75.53938 6.228073
## [1944,] -75.53938 6.228083
## [1945,] -75.53938 6.228088
## [1946,] -75.53938 6.228095
## [1947,] -75.53938 6.228104
## [1948,] -75.53939 6.228107
## [1949,] -75.53939 6.228115
## [1950,] -75.53941 6.228119
## [1951,] -75.53941 6.228121
## [1952,] -75.53942 6.228124
## [1953,] -75.53943 6.228129
## [1954,] -75.53943 6.228130
## [1955,] -75.53944 6.228135
## [1956,] -75.53944 6.228141
## [1957,] -75.53945 6.228144
## [1958,] -75.53945 6.228151
## [1959,] -75.53945 6.228151
## [1960,] -75.53945 6.228152
## [1961,] -75.53946 6.228160
## [1962,] -75.53946 6.228163
## [1963,] -75.53947 6.228167
## [1964,] -75.53947 6.228173
## [1965,] -75.53947 6.228173
## [1966,] -75.53948 6.228182
## [1967,] -75.53948 6.228192
## [1968,] -75.53949 6.228201
## [1969,] -75.53949 6.228210
## [1970,] -75.53950 6.228217
## [1971,] -75.53950 6.228219
## [1972,] -75.53950 6.228222
## [1973,] -75.53951 6.228233
## [1974,] -75.53951 6.228240
## [1975,] -75.53951 6.228242
## [1976,] -75.53951 6.228243
## [1977,] -75.53951 6.228245
## [1978,] -75.53952 6.228251
## [1979,] -75.53952 6.228252
## [1980,] -75.53952 6.228254
## [1981,] -75.53952 6.228261
## [1982,] -75.53953 6.228270
## [1983,] -75.53954 6.228278
## [1984,] -75.53954 6.228283
## [1985,] -75.53954 6.228286
## [1986,] -75.53955 6.228294
## [1987,] -75.53956 6.228307
## [1988,] -75.53956 6.228314
## [1989,] -75.53957 6.228321
## [1990,] -75.53957 6.228330
## [1991,] -75.53958 6.228342
## [1992,] -75.53959 6.228351
## [1993,] -75.53959 6.228352
## [1994,] -75.53959 6.228355
## [1995,] -75.53960 6.228360
## [1996,] -75.53961 6.228364
## [1997,] -75.53963 6.228365
## [1998,] -75.53964 6.228363
## [1999,] -75.53964 6.228363
## [2000,] -75.53965 6.228358
## [2001,] -75.53966 6.228353
## [2002,] -75.53966 6.228346
## [2003,] -75.53967 6.228339
## [2004,] -75.53967 6.228336
## [2005,] -75.53967 6.228332
## [2006,] -75.53967 6.228331
## [2007,] -75.53968 6.228319
## [2008,] -75.53969 6.228304
## [2009,] -75.53969 6.228287
## [2010,] -75.53970 6.228277
## [2011,] -75.53970 6.228267
## [2012,] -75.53970 6.228265
## [2013,] -75.53970 6.228262
## [2014,] -75.53970 6.228251
## [2015,] -75.53971 6.228240
## [2016,] -75.53971 6.228238
## [2017,] -75.53971 6.228237
## [2018,] -75.53971 6.228228
## [2019,] -75.53971 6.228212
## [2020,] -75.53971 6.228212
## [2021,] -75.53971 6.228207
## [2022,] -75.53971 6.228206
## [2023,] -75.53971 6.228201
## [2024,] -75.53971 6.228201
## [2025,] -75.53973 6.228195
## [2026,] -75.53974 6.228182
## [2027,] -75.53974 6.228177
## [2028,] -75.53974 6.228170
## [2029,] -75.53974 6.228158
## [2030,] -75.53974 6.228158
## [2031,] -75.53974 6.228141
## [2032,] -75.53974 6.228140
## [2033,] -75.53974 6.228121
## [2034,] -75.53974 6.228101
## [2035,] -75.53974 6.228099
## [2036,] -75.53974 6.228076
## [2037,] -75.53974 6.228075
## [2038,] -75.53974 6.228071
## [2039,] -75.53974 6.228054
## [2040,] -75.53974 6.228036
## [2041,] -75.53974 6.228033
## [2042,] -75.53974 6.228022
## [2043,] -75.53975 6.228008
## [2044,] -75.53975 6.228004
## [2045,] -75.53975 6.227994
## [2046,] -75.53975 6.227977
## [2047,] -75.53976 6.227974
## [2048,] -75.53976 6.227957
## [2049,] -75.53976 6.227950
## [2050,] -75.53976 6.227945
## [2051,] -75.53976 6.227942
## [2052,] -75.53977 6.227937
## [2053,] -75.53977 6.227920
## [2054,] -75.53977 6.227909
## [2055,] -75.53977 6.227905
## [2056,] -75.53977 6.227894
## [2057,] -75.53977 6.227882
## [2058,] -75.53977 6.227881
## [2059,] -75.53977 6.227869
## [2060,] -75.53977 6.227861
## [2061,] -75.53977 6.227855
## [2062,] -75.53977 6.227849
## [2063,] -75.53977 6.227844
## [2064,] -75.53977 6.227840
## [2065,] -75.53977 6.227831
## [2066,] -75.53977 6.227823
## [2067,] -75.53977 6.227819
## [2068,] -75.53977 6.227810
## [2069,] -75.53977 6.227804
## [2070,] -75.53977 6.227804
## [2071,] -75.53977 6.227789
## [2072,] -75.53977 6.227777
## [2073,] -75.53977 6.227773
## [2074,] -75.53977 6.227766
## [2075,] -75.53976 6.227756
## [2076,] -75.53976 6.227751
## [2077,] -75.53975 6.227747
## [2078,] -75.53974 6.227741
## [2079,] -75.53974 6.227738
## [2080,] -75.53973 6.227734
## [2081,] -75.53973 6.227731
## [2082,] -75.53972 6.227728
## [2083,] -75.53972 6.227726
## [2084,] -75.53971 6.227723
## [2085,] -75.53970 6.227716
## [2086,] -75.53969 6.227708
## [2087,] -75.53968 6.227697
## [2088,] -75.53967 6.227685
## [2089,] -75.53966 6.227672
## [2090,] -75.53965 6.227667
## [2091,] -75.53965 6.227658
## [2092,] -75.53964 6.227644
## [2093,] -75.53964 6.227643
## [2094,] -75.53963 6.227631
## [2095,] -75.53963 6.227618
## [2096,] -75.53962 6.227606
## [2097,] -75.53962 6.227606
## [2098,] -75.53962 6.227606
## [2099,] -75.53962 6.227593
## [2100,] -75.53962 6.227581
## [2101,] -75.53962 6.227573
## [2102,] -75.53962 6.227567
## [2103,] -75.53962 6.227561
## [2104,] -75.53963 6.227553
## [2105,] -75.53963 6.227540
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## [2573,] -75.53918 6.232750
## [2574,] -75.53916 6.232712
## [2575,] -75.53914 6.232671
## [2576,] -75.53913 6.232655
## [2577,] -75.53913 6.232645
## [2578,] -75.53911 6.232616
## [2579,] -75.53910 6.232605
## [2580,] -75.53909 6.232580
## [2581,] -75.53909 6.232567
## [2582,] -75.53909 6.232557
## [2583,] -75.53908 6.232544
## [2584,] -75.53908 6.232536
## [2585,] -75.53907 6.232528
## [2586,] -75.53907 6.232516
## [2587,] -75.53906 6.232500
## [2588,] -75.53906 6.232484
## [2589,] -75.53905 6.232454
## [2590,] -75.53905 6.232445
## [2591,] -75.53904 6.232432
## [2592,] -75.53903 6.232421
## [2593,] -75.53902 6.232407
## [2594,] -75.53901 6.232395
## [2595,] -75.53900 6.232386
## [2596,] -75.53898 6.232372
## [2597,] -75.53897 6.232366
## [2598,] -75.53896 6.232358
## [2599,] -75.53896 6.232352
## [2600,] -75.53895 6.232345
## [2601,] -75.53893 6.232338
## [2602,] -75.53892 6.232332
## [2603,] -75.53891 6.232329
## [2604,] -75.53889 6.232327
## [2605,] -75.53888 6.232324
## [2606,] -75.53887 6.232321
## [2607,] -75.53886 6.232319
##
##
##
## Slot "plotOrder":
## [1] 1
##
## Slot "labpt":
## [1] -75.536780 6.229908
##
## Slot "ID":
## [1] "5"
##
## Slot "area":
## [1] 3.61182e-05
##
##
##
## Slot "plotOrder":
## [1] 5 1 6 2 4 3
##
## Slot "bbox":
## min max
## x -75.609064 -75.531437
## y 6.193665 6.302448
##
## Slot "proj4string":
## CRS arguments:
## +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0library(leaflet)
m <-leaflet(nom_bar)
#factpal <- colorFactor(topo.colors(4), nom_bar$cluster)
factpal <- colorFactor(palette = c("blue", "yellow", "green", "red","purple"), levels = c("1", "2", "3", "4", "5"))
m <- addPolygons(m,popup=nombres_barrios, color=~factpal(cluster))
m <- addTiles(m)
m